用单张图片矫正货架歪斜，提升零售场景图像精度
ShelfRectNet: Single View Shelf Image Rectification with Homography Estimation
- 基于ConvNeXt的网络预测4点仿射变换矩阵
- 测试集角点误差低至1.298像素，优于传统方法
- 适合零售监控、商品陈列等实际应用
从单张图像估计仿射变换仍是具挑战性但极具实用价值的任务，尤其在零售领域，货架监控通常仅能获取单一视角。本文提出一种深度学习框架，通过预测4点参数化仿射矩阵实现任意角度拍摄的货架图像矫正。模型采用ConvNeXt作为主干网络增强特征表示，并使用归一化坐标回归提升稳定性。为缓解数据稀缺问题并促进泛化能力，我们设计了一种新型增强策略，通过建模与采样合成仿射变换。在测试集上，本方法平均角点误差达1.298像素。相较于经典计算机视觉与深度学习方法，该方法在精度和推理速度上均表现优异。结果表明，该方案是真实场景单视图矫正的鲁棒高效解决方案。为推动该领域研究，我们将公开数据集ShelfRectSet及代码。
原文摘要 · Abstract (English)
Estimating homography from a single image remains a challenging yet practically valuable task, particularly in domains like retail, where only one viewpoint is typically available for shelf monitoring and product alignment. In this paper, we present a deep learning framework that predicts a 4-point parameterized homography matrix to rectify shelf images captured from arbitrary angles. Our model leverages a ConvNeXt-based backbone for enhanced feature representation and adopts normalized coordinate regression for improved stability. To address data scarcity and promote generalization, we introduce a novel augmentation strategy by modeling and sampling synthetic homographies. Our method achieves a mean corner error of 1.298 pixels on the test set. When compared with both classical computer vision and deep learning-based approaches, our method demonstrates competitive performance in both accuracy and inference speed. Together, these results establish our approach as a robust and efficient solution for realworld single-view rectification. To encourage further research in this domain, we will make our dataset, ShelfRectSet, and code publicly available
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